rm save_inference_model (#2133)

modules/image/Image_editing/super_resolution/dcscn/README.md

@@ -62,7 +62,6 @@
   #visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/dcscn/README_en.md

@@ -64,7 +64,6 @@
     im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
     res = sr_model.reconstruct(images=[im], visualization=True)
     print(res[0]['data'])
-    sr_model.save_inference_model()
     ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_a/README.md

@@ -62,7 +62,6 @@
   #visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_a/README_en.md

@@ -65,7 +65,6 @@
     im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
     res = sr_model.reconstruct(images=[im], visualization=True)
     print(res[0]['data'])
-    sr_model.save_inference_model()
     ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_b/README.md

@@ -62,7 +62,6 @@
   #visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_b/README_en.md

@@ -65,7 +65,6 @@
   im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_c/README.md

@@ -62,7 +62,6 @@
   #visualization=True可以用于查看超分图片效果，可设置为False提升运行速度。
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/Image_editing/super_resolution/falsr_c/README_en.md

@@ -65,7 +65,6 @@
   im = cv2.imread('/PATH/TO/IMAGE').astype('float32')
   res = sr_model.reconstruct(images=[im], visualization=True)
   print(res[0]['data'])
-  sr_model.save_inference_model()
   ```
 
 - ### 3、API

modules/image/semantic_segmentation/humanseg_lite/README.md

@@ -62,8 +62,6 @@
     res = human_seg.segment(images=[im],visualization=True)
     print(res[0]['data'])
     human_seg.video_segment('/PATH/TO/VIDEO')
-    human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
     ```
     - 视频流预测代码示例：
 

modules/image/semantic_segmentation/humanseg_lite/README_en.md

@@ -65,8 +65,6 @@
         res = human_seg.segment(images=[im],visualization=True)
         print(res[0]['data'])
         human_seg.video_segment('/PATH/TO/VIDEO')
-        human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
         ```
     - Video prediction example:
 

modules/image/semantic_segmentation/humanseg_mobile/README.md

@@ -66,8 +66,6 @@
     res = human_seg.segment(images=[im],visualization=True)
     print(res[0]['data'])
     human_seg.video_segment('/PATH/TO/VIDEO')
-    human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
     ```
     - 视频流预测代码示例：
 

modules/image/semantic_segmentation/humanseg_mobile/README_en.md

@@ -65,8 +65,6 @@
         res = human_seg.segment(images=[im],visualization=True)
         print(res[0]['data'])
         human_seg.video_segment('/PATH/TO/VIDEO')
-        human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
         ```
     - Video prediction example:
 

modules/image/semantic_segmentation/humanseg_server/README.md

@@ -65,8 +65,6 @@
     res = human_seg.segment(images=[im],visualization=True)
     print(res[0]['data'])
     human_seg.video_segment('/PATH/TO/VIDEO')
-    human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
     ```
     - 视频流预测代码示例：
 

modules/image/semantic_segmentation/humanseg_server/README_en.md

@@ -63,8 +63,6 @@
         res = human_seg.segment(images=[im],visualization=True)
         print(res[0]['data'])
         human_seg.video_segment('/PATH/TO/VIDEO')
-        human_seg.save_inference_model('/PATH/TO/SAVE/MODEL')
-
         ```
     - Video prediction example: